Principle:Mistralai Client python Finetuning Job Monitoring

Knowledge Sources	Mistral Fine-tuning Mistral Client Python
Domains	Fine_Tuning, Job_Management
Last Updated	2026-02-15 14:00 GMT

Overview

A polling-based monitoring pattern that tracks fine-tuning job progress by querying job status and training metrics.

Description

Finetuning Job Monitoring involves repeatedly querying the job status to track training progress. The get method retrieves a specific job's details (status, metrics, checkpoints), while list retrieves all jobs with optional filtering. Job statuses include QUEUED, RUNNING, SUCCEEDED, FAILED, CANCELLED, and others. Monitoring enables detecting completion, failures, and intermediate training metrics.

Usage

Use this principle after creating a fine-tuning job. Poll fine_tuning.jobs.get(job_id=...) periodically to check status, or use fine_tuning.jobs.list() to see all jobs. Check for SUCCEEDED or FAILED to determine when to proceed.

Theoretical Basis

Polling-based job monitoring:

1. Create job and receive job_id
2. Periodically poll get(job_id=...) for status
3. Check status transitions: QUEUED → RUNNING → SUCCEEDED/FAILED
4. On SUCCEEDED: extract fine_tuned_model name from response
5. On FAILED: check error details for debugging

Related Pages

Implemented By

• Implementation:Mistralai_Client_python_FineTuningJobs_Get_List